title: Identifying driver mutations from sequencing data of heterogeneous tumors in the era of personalized genome sequencing.
abstract: Distinguishing driver mutations from passenger mutations is critical to the understanding of the molecular mechanisms of carcinogenesis and for identifying prognostic and diagnostic markers as well as therapeutic targets. We reviewed the current approaches and software for identifying driver mutations from passenger mutations including both biology-based approaches and machine-learning-based approaches. We also reviewed approaches to identify driver mutations in the context of pathways or gene sets. Finally, we discussed the challenges of predicting driver mutations considering the complexities of inter- and intra-tumor heterogeneity as well as the evolution and progression of tumors.
authors.affiliation: Hangzhou, 310003, China; biaoylin@gmail.com.
authors.forename: Jing
authors.initials: J
authors.lastname: Zhang
authors.forename: Jie
authors.initials: J
authors.lastname: Liu
authors.forename: Jianbo
authors.initials: J
authors.lastname: Sun
authors.forename: Chen
authors.initials: C
authors.lastname: Chen
authors.forename: Gregory
authors.initials: G
authors.lastname: Foltz
authors.forename: Biaoyang
authors.initials: B
authors.lastname: Lin
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods; D004252:DNA Mutational Analysis / Q000706:statistics & numerical data; D059014:High-Throughput Nucleotide Sequencing / Q000706:statistics & numerical data; D006801:Humans; D016013:Likelihood Functions; D009154:Mutation; D009369:Neoplasms / Q000235:genetics*; D020641:Polymorphism, Single Nucleotide; D057285:Precision Medicine; D012984:Software
keywords: SNPs; cancer; driver mutation; next-generation sequencing; passenger mutation